Input-output linearization of DC-DC converter with discrete sliding mode fuzzy control strategy

The major thrust of the paper is on designing a fuzzy logic approach has been combined with a well-known robust technique discrete sliding mode control (DSMC) to develop a new strategy for discrete sliding mode fuzzy control (DSMFC) in direct current (DC-DC) converter. Proposed scheme requires human expertise in the design of the rule base and is inherently stable. It also overcomes the limitation of DSMC, which requires bounds of uncertainty to be known for development of a DSMC control law. The scheme is also applicable to higher order systems unlike model following fuzzy control, where formation of rule base becomes difficult with rise in number of error and error derivative inputs. In this paper the linearization of input-output performance is carried out by the DSMFC algorithm for boost converter. The DSMFC strategy minimizes the chattering problem faced by the DSMC. The simulated performance of a discrete sliding mode fuzzy controller is studied and the results are investigated.

Design of frequency reconfigurable planar antenna using artificial neural network

In this paper, the design of frequency reconfigurable planar antenna by incorporation of metasurface superstrate (FRPA-MSS) is presented using an artificial neural network. The dual-layer radiating structure is created on a 1.524 mm thick Rogers RO4350B substrate board (εr = 3.48, tan δ = 0.0037). The candidate antenna is designed and analyzed using a high-frequency structure simulator (HFSS) tool. The transfer matrix method is employed for the successful retrieval of electromagnetic properties of the metamaterial. Frequency reconfiguration is achieved by placing the metasurface superstrate onto the rectangular patch antenna. A simplified ANN approach has been employed for the design of metasurface incorporated proposed antenna. Presented prototypes are characterized through experimental measurements. It is found from the practical observations that the proposed antenna effectively reconfigures the tuning range from 5.03 to 6.13 GHz. Moreover, the presented antenna operates efficiently with agreeable gain, good impedance matching, and stable pattern characteristics across the entire operational bandwidth. The experimental results obtained validate the simulated performance.

A Case Study of Rock Type Prediction Using Random Forests: Erdenet Copper Mine, Mongolia

In a mine, knowledge of rock types is often desired as they are important indicators of grade, mineral processing complications, or geotechnical attributes. It is common to model the rock types with visual graphics tools using geologist-generated rock type information in exploration drillhole databases. Instead of this manual approach, this paper used random forest (RF), a machine learning (ML) algorithm, to model the rock type at Erdenet Copper Mine, Mongolia. Exploration drillhole data was used to develop the RF models and predict the rock type based on the coordinates of locations. Data selection and model evaluation methods were designed to ensure applicability for real life scenarios. In the scenario where rock type is predicted close to locations where information is available (such as in blocks being blasted), RF did very well with an overall success rate (OSR) of 89%. In the scenario where rock type was predicted for two future benches (i.e., 30 m below known locations), the best OSR was 86%. When an exploration program was simulated, performance was poor with a OSR of 59%. The results indicate that EMC can leverage RF models for short-term and long-term planning by predicting rock types within drilling blocks or future blocks quite accurately.